End-to-End Implementation & Configuration
End-To-End Implementation & Configuration
Implementing Datadog correctly from the start makes the difference between a platform that delivers value from day one and an underused tool that racks up costs with no return. At Apiwan, we configure the platform comprehensively, covering every pillar of modern observability with a methodical, repeatable approach aligned with industry best practices.
We don’t do quick installs: we design observability architectures that grow alongside the business.
Who is this service for?
Ideal for organizations that:
- Are adopting Datadog for the first time and want to get it right from the start.
- Have a partial implementation and need to extend it to new teams, services, or technology layers.
- Migrated to the cloud or Kubernetes and their previous monitoring stack has become obsolete.
- Are looking to replace fragmented monitoring tools (Zabbix, Nagios, Splunk, New Relic) with a unified platform.
Capabilities we implement
Infrastructure and Application Observability
Infrastructure Monitoring Full coverage of the infrastructure estate: physical and virtual servers, cloud instances (AWS, Azure, GCP), Docker and Kubernetes containers, serverless functions, and network. Datadog Agent configuration with autodiscovery, integration with major cloud providers, and automatic correlation across layers.
APM — Application Performance Monitoring Application instrumentation for end-to-end visibility into every request: distributed traces, service dependency maps, automatic bottleneck detection, and correlation with logs and infrastructure. Compatible with major languages and frameworks (Java, Python, Node.js, .NET, Go, Ruby, and more).
Log Management Log pipeline design: ingestion from multiple sources (Agent, Forwarder, Kinesis Firehose, syslog), parsing and enrichment, index and retention policy definition, Flex Logs configuration for low-cost long-term retention, and alerts on log patterns.
Database Monitoring (DBM) Deep visibility into database behavior: slow queries, execution plans, connection metrics, locks, and anomalies. Compatible with PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB.
Network Monitoring Visibility into traffic between services, hosts, and containers: Network Performance Monitoring (NPM), DNS Monitoring, and Cloud Network Maps to understand real network dependencies in complex environments.
Digital Experience
Real User Monitoring (RUM) Instrumentation of web and mobile applications to capture each user’s real experience: load times, frontend errors, Core Web Vitals, and correlation with backend traces for end-to-end diagnosis.
Synthetic Monitoring Proactive, automated testing of critical business journeys (login, checkout, APIs) from multiple geographic locations — catching issues before users even report them. Private Locations configuration to monitor internal or staging environments.
Session Replay Playback of user sessions to diagnose friction points, errors, and unexpected interface behavior. Combined with RUM, it lets you correlate the visual experience with the underlying technical data.
Software Delivery & DevOps
CI Visibility — Pipeline & Test Observability Full visibility into continuous integration pipelines: execution times by stage, failure rates, flaky test detection, and engineering metrics (DORA Metrics). Integration with major CI providers (GitHub Actions, GitLab CI, Jenkins, CircleCI, Azure Pipelines). Enables engineering teams to identify what’s slowing down software delivery and make data-driven decisions.
Test Optimization Analysis and optimization of test suites: identifying slow, redundant, or flaky tests, and correlating test results with deployments and code changes.
Artificial Intelligence & LLMs
LLM Observability Monitoring and evaluation of applications and agents built on large language models (LLMs). Apiwan implements LLM Observability for organizations developing or integrating generative AI capabilities into their products.
Includes: prompt and response traces, token usage and latency metrics per model, output quality evaluation (hallucination detection, relevance, coherence), and detection of security risks such as PII exposure and prompt injection.
Compatible with major models and frameworks: OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, LangChain, LlamaIndex, CrewAI, and more.
Methodological approach
Apiwan’s implementation follows a phased, structured process that minimizes production risk and guarantees measurable results at every stage:
| Phase | Activity | Outcome |
|---|---|---|
| 1. Discovery | Architecture survey and scope definition | Agreed scope and implementation backlog |
| 2. Foundations | Agent deployment, cloud integrations, baseline tags | Infrastructure visible in Datadog |
| 3. Instrumentation | APM, Logs, RUM, DBM, CI as scoped | Full-stack observability active |
| 4. Alerting & Dashboards | Monitors, SLOs, and operational dashboards | Operations with visibility and useful alerts |
| 5. Handover | Documentation, training, and knowledge transfer | Team autonomous to operate the platform |
